Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity

Abstract

The performance of two computer programs, DEREK and TOPKAT, was examined with regard to predicting the outcome of the Ames bacterial mutagenicity assay. The results of over 400 Ames tests conducted at Glaxo Wellcome (now GlaxoSmithKline) during the last 15 years on a wide variety of chemical classes were compared with the mutagenicity predictions of both computer programs. DEREK was considered concordant with the Ames assay if (i) the Ames assay was negative (not mutagenic) and no structural alerts for mutagenicity were identified or (ii) the Ames assay was positive (mutagenic) and at least one structural alert was identified. Conversely, the DEREK output was considered discordant if (i) the Ames assay was negative and any structural alert was identified or (ii) the Ames assay was positive and no structural alert was identified. The overall concordance of the DEREK program with the Ames results was 65% and the overall discordance was 35%, based on over 400 compounds. About 23% of the test molecules were outside the permissible limits of the optimum prediction space of TOPKAT. Another 4% of the compounds were either not processable or had indeterminate mutagenicity predictions; these molecules were excluded from the TOPKAT analysis. If the TOPKAT probability was (i) > or =0.7 the molecule was predicted to be mutagenic, (ii) < or =0.3 the compound was predicted to be non-mutagenic and (iii) between 0.3 and 0.7 the prediction was considered indeterminate. From over 300 acceptable predictions, the overall TOPKAT concordance was 73% and the overall discordance was 27%. While the overall concordance of the TOPKAT program was higher than DEREK, TOPKAT fared more poorly than DEREK in the critical Ames-positive category, where 60% of the compounds were incorrectly predicted by TOPKAT as negative but were mutagenic in the Ames test. For DEREK, 54% of the Ames-positive molecules had no structural alerts and were predicted to be non-mutagenic. Alternative methods of analyzing the output of the programs to increase the accuracy with Ames-positive compounds are discussed.

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

“…The three structure-based systems produced equivalent results (71-76% concordance), whereas the physicochemical system produced a lower (61%) concordance. Similar results for DfW and TOPKAT were reported by Cariello et al (2002) the accuracy of prediction of Ames mutagenicity by DfW was 65% (against a dataset of 400 GlaxoSmithKline chemicals). The overall concordance for TOPKAT was 73% but it should also be noted that TOPKAT was capable to predict 300 out of the 400 chemicals.…”

Mutagenic and carcinogenic structural alerts and their mechanisms of action

“…Structural alerts causing genotoxicity are composed of mutagenicity and structural alerts based on data from several in vitro and in vivo mutagenicity tests and other genotoxicity data. The software was developed for research and industry users in collaboration with industry, academia, and regulatory authorities (2,60).…”

“…Mutagenicity and carcinogenicity models include data derived from bacterial mutagenicity and rodent carcinogenicity tests. The mutagenicity model is based on data for 393 chemicals from the US EPA GeneTox protocol (60).…”

Toward a greater appreciation of noncovalent chemical/DNA interactions: Application of biological and computational approaches

“…With tens of thousands of chemicals and drugs having been so evaluated for covalent DNA interactions, it has been possible to develop structure-activity relationships (SARs) defining the mutagenicity of novel molecules based on either manual inspection or computational recognition of structural alerts for DNA reactivity or Ashby alerts [Ashby, 1985]. Computational programs such as DEREK, MCASE, and TOPKAT [Cariello et al, 2002;Greene, 2002] differ somewhat with respect to the nature of their output, but each relies on information from a learning set consisting primarily of two-dimensional chemical structure and associated bacterial mutagenicity findings to identify those chemical moieties for which a link to mutagenicity has been reported. Not all DNA-reactive molecules form covalent adducts, and not all covalent adducts are genotoxic.…”